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• W4212865066 abstract "Discriminating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) can help provide more specific treatments. However, there are no ideal biomarkers for their differentiation. Thus, the aim of this study was to identify biomarkers for diagnosing and predicting the progression of DN by investigating different salivary glycopatterns. Lectin microarrays were used to screen different glycopatterns in patients with DN or NDRD. The results were validated by lectin blotting. Logistic regression and artificial neural network analyses were used to construct diagnostic models and were validated in an external cohort. Pearson’s correlation analysis, Cox regression, and Kaplan–Meier survival curves were used to analyse the correlation between lectins and disease severity and progression. The normalised fluorescent intensities (NFIs) for 37 lectins are summarised as the mean ± 95% confidence interval (CI) (Fig. 2a).Hierarchical clustering analysis and PCA of lectins with significant differentiation of NFIs between DN and NDRD(Fig. 2b,c). The lectin blotting was used to confirm the above results(Fig. 2d,e).Both the logistic regression model and the artificial neural network model achieved high diagnostic accuracy (Table1). The levels of Aleuria aurantia lectin (AAL), Lycopersicon esculentum lectin (LEL), Lens culinaris lectin (LCA), Vicia villosa lectin (VVA), and Narcissus pseudonarcissus lectin (NPA) were significantly correlated with the clinical parameters related to DN severity (Table2). A high level of LCA and low level of LEL were associated with a higher risk of progression to end-stage renal disease (figure1,f). Table1. Detailed information regarding the ROC analysis of the constructive models in the training cohort and validation cohort with logistic regression and artificial neural network analysis, respectively. Tabled 1SensitivitySpecificityAccuracyAUCOptimal cutoff valueLogistic Regression AnalysisTraining Cohort0.9500.7000.8100.8920.246Validation Cohort0.9200.6900.8000.8670.331Artificial Neural Network AnalysisTraining Cohort1.0001.0001.0001.0001.500Validation Cohort0.7700.8600.8200.8161.500 Open table in a new tab Table2. Pearson correlation of expression levels of glycopatterns in the saliva and clinical parameters related to the severity of diabetic nephropathy. Tabled 1Proteinuria (g/24h)BUN (mmol/L)eGFR (ml/min/1.73m2)Scr (μmol/L)Classes of glomerular lesionsScores of Interstitial and vascular lesionsEEL0.049-0.040-0.1790.1110.0660.160AAL-0.119-0.215*0.102-0.0550.005-0.084LTL0.130-0.085-0.0450.0950.0360.097LEL-0.084-0.350***0.884***-0.768***-0.739***-0.769***DBA0.027-0.0380.009-0.004-0.0230.010LCA0.0430.402***-0.757***0.783***0.573***0.769***VVA0.606***-0.013-0.377***0.450***0.276**0.486***NPA-0.038-0.0840.192-0.163-0.206*-0.167ACA-0.025-0.1650.097-0.0300.023-0.084PWM-0.0560.0770.092-0.1130.039-0.138BPL-0.019-0.1380.021-0.081-0.013-0.046PHA-0.0250.057-0.0440.0400.1020.011 Open table in a new tab Notes: The estimated glomerular filtration rate (eGFR) was calculated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. *p<0.05; **p<0.01; and ***p<0.001. BUN, blood urea nitrogen; Scr, serum creatinine Glycopatterns in the saliva could be a non-invasive tool for distinguishing between DN and NDRD. The AAL, LEL, LCA, VVA, and NPA levels could reflect the severity of DN, and the LEL and LCA levels could reflect the prognosis of DN." @default.
• W4212865066 created "2022-02-24" @default.
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• W4212865066 date "2022-02-01" @default.
• W4212865066 modified "2023-09-27" @default.
• W4212865066 title "POS-368 Salivary glycopatterns as potential non-invasive biomarkers of diabetic nephropathy" @default.
• W4212865066 doi "https://doi.org/10.1016/j.ekir.2022.01.390" @default.
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